tesseract-ocr

To improve OCR accuracy on scanned documents, apply deterministic preprocessing like resizing, denoising, and deskewing to normalize images. These steps optimize OCR readiness and directly reduce recognition errors before the engine processes the page.

Page segmentation mode (PSM) tuning controls how the engine divides a document into text regions. You need PSM tuning for data extraction when processing complex layouts, ensuring the engine recognizes text blocks accurately rather than treating a page as a single block.

To handle multi-language scans with Tesseract, manage language packs by loading the required linguistic data for your documents. This allows the engine to accurately recognize and extract text containing mixed languages during the OCR pipeline.

Yes, Tesseract OCR can be used for production deployments requiring consistent latency. The pipeline includes monitoring, logging, and guardrails to ensure scalable OCR workflows maintain reliable performance and output quality across varied document types.

OCR fails on poor quality images due to noise, skew, or low resolution that obscures character shapes. Post-processing quality checks help by validating the extracted data, catching errors from degraded inputs that survived the initial preprocessing stage.